AI-Based Malaria Detection Updates and Challenges in Endemic Regions

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Malaria remains a significant global health challenge, with limited trained lab technicians and insufficient microscopy resources. Artificial Intelligence (AI) solutions show promise in improving timeliness and accuracy of malaria detection. The need for standardized benchmarking of AI models and improved access to quality datasets are highlighted. The Topic Group on Malaria is actively discussing enhancements and launching challenges to advance disease surveillance and prediction capabilities.

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FGAI4H-J-014-A03 E-meeting, 30 September 2 October 2020 Source: TG-Malaria Topic Driver Title: Att.3 Presentation (TG-Malaria) Purpose: Discussion Contact: Rose Nakasi Makerere University Uganda This PPT contains an update on TG-Malaria and presentation of J-014-A01. E-mail: g.nakasirose@gmail.com Abstract:

Topic Group-Malaria: AI based detection of Malaria-an update Rose Nakasi Makerere University, Uganda E-meeting, 30 September 2 October 2020

Background Malaria burden in endemic Countries Accounts for over 3.4 billion cases globally Lack of enough trained lab technicians 1.72 microscopes per 100,000 population, but only 0.85 Microscopists per 100,000 Gold standard diagnosis(microscopy) challenge SOP requires not to view more than 30 slides a day Less diagnosis throughput Variations in individual expert judgment AI solution Supports image analysis and has potential to improve the timeliness and accuracy There is need to; Standardise benchmarking AI solutions for the detection of Malaria

Digital imaging Setup

TG-Malaria activities Quality datasets needed Have more datasets for training and testing Well labelled datasets Solution AI models and approaches related to malaria detection. Suggestions on scoring metrics. Improvements on the benchmarking framework. Support to the group on different aspects (data, methods, benchmarking, etc.) of this topic Extension of the solution to improve disease surveillance and prediction. Heterogeneous Data needed

Topic Group members: Name Affiliation Philippe Verstraete Co-founder of Milan and Associates, Italy Laura Moro, Researcher, science & medical writer. Co-founder of AI Scope, Spain Cofounder of AIME company, Malaysia Researcher at University of Dodoma, Tanzania Dr. Helmi Zakariah. Martha Shaka Ana Rivi re CinnamondAdvisor and Pubic Health Expert under Health Emergency Information & Risk Assessment Department with PAHO/WHO. Senior Access Officer, FIND, Switetzerland Rigveda Kadam Scientific Officer, FIND, Switetzerland Seda Yerlikaya Herilalaina RAKOTOARISON PhD student in Machine learning from the Universit Paris- Saclay) AI for Outbreak Detection - FG-AI4H 6

Updates since meeting I Holding bi-weekly online meetings to discuss benchmarking improvements Publications related to Topic group Second Minimal Benchmarking platform developed Launching challenge Updates available in TDD

TG-Malaria Online meetings Online Skype meetings Discuss updates on benchmarking platform improvements (data, AI models, Interface) Discuss technical implementation details that come with improvements Develop simple models for testing the updated benchmarking platform Platform beta testing Launching the challenge

Some publications A new approach for microscopic diagnosis of malaria parasites in thick blood smears using pre-trained deep learning models (Springer SN Applied Sciences 2, 1255, 2020)

Some publications An approach for Assessing quality of labeled Data for a machine learning task in Malaria detection; (Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies: PP. 301- 304, 2020. )

Some publications A web-based intelligence platform for diagnosis of malaria in thick blood smear images: A case for a developing Country (Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 984-985, 2020

First Benchmarking Platform v1 Our first benchmarking attempt required the following; Inputs Outputs Scores & Metrics ROC AUC Precision recall Average precision Report Performance of AI models A well annotated dataset of thick blood smear images AI models to be submitted to the benchmarking platform Performance of AI

Update on Benchmarking platform-V2 Add new public dataset of thin blood smear dataset A well annotated dataset of thin blood smear images A total of 27,558 images Infected and uninfected cells Adding support for deep learning library (pytorch and tensorflow) Cclassification tasks Setting up time budget for up-to 1 hour/submissionn. To allow for heavy submissions Adding support for uploading dataset Towards crowdsourcing

User interface for the challenge Benchmarking-Malaria platform with codalab

Support for uploading dataset Support for uploading dataset

Scoring metrices Preliminary results

Benchmarking Malaria platform-v2: https://codalab.lri.fr/competitions/775)

Next benchmarking iterations We have achieved a second version attempt on the benchmarking platform, Our next set of activities include; Receive participant feedback from our first challenge Assess performance of models Investigate possibility for scaling to object detection tasks.

Next steps Data Collection Undisclosed datasets needed for testinng Updates to TDD TG building Encourage TG member participation Get more experts involved

Call for participation Participation can be in form of: Provision of quality labelled data AI models and algorithms for benchmarking task on malaria General support on different aspects of this topic (data, methods, benchmarking, etc.) AI for Outbreak Detection - FG-AI4H 20